Yearly Traffic Safety Analysis

416 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Cedar County recorded 416 total traffic crashes, a 9.8% increase from the 379 crashes reported in 2023. While overall crashes rose, total fatalities decreased from 5 to 4. The most significant year-over-year shift was a 62% increase in the number of single-vehicle, non-collision incidents, which grew from 138 to 224.

416

▲ 9.8%was 379

Total Crash Events

4

▼ -20.0%was 5

Persons Killed

89

▼ -7.3%was 96

Persons Injured

4

▼ -20.0%was 5

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Cedar County showed an upward trend, increasing by 9.8% from 379 in 2023 to 416 in 2024. Despite the increase in the total number of crashes, the outcomes were less severe on average. The number of fatalities fell from 5 to 4, and the total number of injuries decreased from 96 to 89.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 4→0.0%

89

Motorists Injured

Prior: 95↓-6.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted year-over-year. The peak day for crashes moved from Thursday (68 crashes) in 2023 to Friday (94 crashes) in 2024. A more pronounced change occurred in the peak hour, which shifted from the 5 p.m. evening commute hour in the prior period (33 crashes) to the 6 a.m. morning commute hour in the current period (32 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the overall severity of incidents slightly decreased. The number of fatal crashes dropped from 5 in 2023 to 4 in 2024, representing a decline in the fatal crash rate from 1.3% to 1.0% of all crashes. The proportion of crashes resulting in any level of injury also fell from 19.1% to 17.5%, while the share of non-injury crashes rose from 79.7% to 81.5%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1%
↓-20.0%prior 5
Serious Injury10serious injury crashes2.4%
↓-16.7%prior 12
Minor Injury25minor injury crashes6%
↓-19.4%prior 31
Possible Injury38possible injury crashes9.1%
↑31.0%prior 29
No Injury339no injury crashes81.5%
↑12.3%prior 302

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions with animals remained the leading contributing factor in both periods, though the count of such incidents decreased from 98 to 89. The most substantial change was in crashes attributed to "Driving too fast for conditions," which saw a 153% increase in count from 19 to 48, moving it from the fifth to the second-ranked factor. In contrast, crashes involving "Lost Control" decreased in count from 36 to 26.

Officer-Reported Primary Contributing Cause

Animal89 (21.4%)↓-9.2%prior 98
Driving too fast for conditions48 (11.5%)↑152.6%prior 19
Ran off road - straight41 (9.9%)↑28.1%prior 32
Ran off road - left36 (8.7%)↑140.0%prior 15
Followed too close26 (6.3%)↓-13.3%prior 30
Lost Control26 (6.3%)↓-27.8%prior 36
Driver Distraction: Other interior distraction17 (4.1%)↓-5.6%prior 18
FTYROW: From stop sign13 (3.1%)↓-18.8%prior 16
Operating vehicle in an reckless, erratic, careless, negligent manner10 (2.4%)↓-23.1%prior 13
Driver Distraction: Inattentive/lost in thought10 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both years occurred in daylight and on dry roads, there was a marked increase in crashes under adverse conditions in 2024. The count of crashes on roads with snow more than doubled from 20 to 46. Additionally, crashes occurring in dark, unlighted conditions increased by 49%, from 61 incidents in 2023 to 91 in 2024.

Weather

Clear198 (59.8%)
↑0.5%prior 197
Cloudy54 (16.3%)
↑17.4%prior 46
Snow28 (8.5%)
↑3.7%prior 27
Rain19 (5.7%)
↑171.4%prior 7
Blowing Snow18 (5.4%)
Sleet, hail4 (1.2%)
Fog, smoke, smog4 (1.2%)
Freezing rain/drizzle3 (0.9%)
↓-50.0%prior 6
Severe Winds3 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight206 (61.7%)
↓-0.5%prior 207
Dark - roadway not lighted91 (27.2%)
↑49.2%prior 61
Dark - roadway lighted14 (4.2%)
↑16.7%prior 12
Dusk11 (3.3%)
↑120.0%prior 5
Dawn10 (3.0%)
Dark - unknown roadway lighting2 (0.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry200 (60.4%)
↓-5.7%prior 212
Snow46 (13.9%)
↑130.0%prior 20
Wet39 (11.8%)
↑56.0%prior 25
Ice/frost28 (8.5%)
↑133.3%prior 12
Gravel14 (4.2%)
↓-12.5%prior 16
Slush3 (0.9%)
Other (explain in narrative)1 (0.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet vehicles were the most frequently involved in crashes during both periods. After combining variations in reporting (CHEV/CHEVROLET), Chevrolet-made vehicles were involved in 98 crashes, surpassing Ford (88 vehicles) as the most common make in 2024. The involvement of Freightliner trucks also grew, with the count rising from 28 to 41. Regarding driver demographics, individuals in the 35-44 age group represented a larger share of persons involved in crashes compared to the previous year.

Top Vehicle Makes (604 vehicles)

1
FORD88 (14.6%)
↓-6.4%prior 94
2
CHEV53 (8.8%)
↓-10.2%prior 59
3
CHEVROLET45 (7.5%)
↑60.7%prior 28
4
FREIGHTLINER41 (6.8%)
↑46.4%prior 28
5
GMC25 (4.1%)
↑66.7%prior 15
6
NR23 (3.8%)
↑64.3%prior 14
7
TOYT22 (3.6%)
↑15.8%prior 19
8
HOND21 (3.5%)
↑16.7%prior 18
9
VOLVO20 (3.3%)
↑122.2%prior 9
10
JEEP20 (3.3%)
↑53.8%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

63 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (385 persons with recorded sex)

Male277 (71.9%)
↓-16.8%prior 333
Female108 (28.1%)
↓-40.7%prior 182

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 416
  • Total persons involved: 628
  • Total vehicles involved: 604

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2024-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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